对新闻故事反应中的移情和苦恼的建模
Modeling Empathy and Distress in Reaction to News Stories
摘要：
移情的计算检测和理解是推进人机交互的重要因素。然而，到目前为止，基于文本的共情预测有以下主要局限性：它低估了现象的心理复杂性，坚持一个薄弱的概念，地面真理的共情状态归因于第三方，并缺乏一个共享的语料库。相比之下，这项贡献提出了第一个公开可用的共情预测金标准。它是使用一种新的注释方法，可靠地捕捉移情评估的作家的声明，使用多项规模。这也是第一个区分多种形式的移情、移情关怀和个人痛苦的计算工作，正如整个心理学所认识的那样。最后，我们给出了三种不同预测模型的实验结果，其中CNN表现最好。
英文摘要：
Computational detection and understanding of empathy is an important factor in advancing human-computer interaction. Yet to date, text-based empathy prediction has the following major limitations: It underestimates the psychological complexity of the phenomenon, adheres to a weak notion of ground truth where empathic states are ascribed by third parties, and lacks a shared corpus. In contrast, this contribution presents the first publicly available gold standard for empathy prediction. It is constructed using a novel annotation methodology which reliably captures empathy assessments by the writer of a statement using multi-item scales. This is also the first computational work distinguishing between multiple forms of empathy, empathic concern, and personal distress, as recognized throughout psychology. Finally, we present experimental results for three different predictive models, of which a CNN performs the best.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
